Adaptive fuzzy approach for non-linearity compensation in MEMS gyroscope

In the practical control of microelectromechanical system (MEMS) gyroscopes, dead-zone non-linearity often exists, which has negative influence on the resolution and performance of the gyroscope system. System non-linearities are inevitable in actual MEMS gyroscopes and require the controller to be either adaptive or robust to these non-linearities. In this paper, an adaptive fuzzy compensator is designed to compensate the dead-zone non-linearities for MEMS gyroscope. A fuzzy logic system is used for dead-zone non-linear switching function and an optimal algorithm is designed to make the dead-zone compensator to tune the parameters adaptively. The closed-loop stability can be guaranteed with the proposed adaptive fuzzy dead-zone compensation. Simulation results demonstrate that the tracking error can be attenuated efficiently and robustness of the control system can be improved with the proposed adaptive fuzzy non-linearity compensator.

[1]  Frank L. Lewis,et al.  Deadzone compensation in motion control systems using adaptive fuzzy logic control , 1997, Proceedings of International Conference on Robotics and Automation.

[2]  Frank L. Lewis,et al.  Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities , 1987 .

[3]  Roberto Oboe,et al.  Automatic Mode Matching in MEMS Vibrating Gyroscopes Using Extremum-Seeking Control , 2009, IEEE Transactions on Industrial Electronics.

[4]  Jun Oh Jang,et al.  A deadzone compensator of a DC motor system using fuzzy logic control , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[5]  J.D. John,et al.  Novel concept of a single-mass adaptively controlled triaxial angular rate sensor , 2006, IEEE Sensors Journal.

[6]  C. Batur,et al.  Sliding mode control of a simulated MEMS gyroscope , 2005, Proceedings of the 2005, American Control Conference, 2005..

[7]  Peng-Yung Woo,et al.  An adaptive fuzzy sliding mode controller for robotic manipulators , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[8]  S. H. Park,et al.  Robust-tracking control for robot manipulator with deadzone and friction using backstepping and RFNN controller , 2011 .

[9]  Shengyuan Xu,et al.  Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems , 2011, IEEE Transactions on Fuzzy Systems.

[10]  Roberto Horowitz,et al.  Trajectory-Switching Algorithm for a MEMS Gyroscope , 2007, IEEE Transactions on Instrumentation and Measurement.

[11]  Byung Kook Yoo,et al.  Adaptive control of robot manipulator using fuzzy compensator , 2000, IEEE Trans. Fuzzy Syst..

[12]  Robert Patton Leland,et al.  Adaptive control of a MEMS gyroscope using Lyapunov methods , 2006, IEEE Transactions on Control Systems Technology.

[13]  Sangkyung Sung,et al.  On the Mode-Matched Control of MEMS Vibratory Gyroscope via Phase-Domain Analysis and Design , 2009, IEEE/ASME Transactions on Mechatronics.

[14]  Celal Batur,et al.  A novel adaptive sliding mode control with application to MEMS gyroscope. , 2009, ISA transactions.

[15]  Rong-Jong Wai,et al.  Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Jun Oh Jang,et al.  Deadzone compensation of an XY-positioning table using fuzzy logic , 2005, IEEE Trans. Ind. Electron..

[17]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[18]  P. X. Liu,et al.  Robust Adaptive Fuzzy Output Feedback Control System for Robot Manipulators , 2011, IEEE/ASME Transactions on Mechatronics.